Directional/complex wells

Data-Driven Approach Detects, Classifies Screenout in Horizontal Wells

This paper presents a screenout-classification system based on Gaussian hidden Markov models that predicts screenouts and provides early warning.

Profile of the simulated horizontal well.
Fig. 1—Profile of the simulated horizontal well. (a) The horizontal well trajectory in 3D view; (b) lateral length of the well landing in the pay zone (Niobrara B).

Multistage hydraulic fracturing has gained global popularity as more tight geologic formations are developed economically for hydrocarbon resources. However, screenout is a major issue caused by the blockage of proppant inside the fractures. The complete paper presents a screenout-classification system based on Gaussian hidden Markov models (GHMMs) trained on simulated data that predicts screenouts and provides early warning by learning prescreenout patterns in surface-pressure signals. The methodology is a useful tool for early screenout detection and shows the promise of other fracturing time-series data analysis.

Materials and Methods

In the complete paper, fracturing treatment data are generated using a hydraulic fracturing simulation software.

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